Large-sample results for optimization-based clustering methods

Many common (nonhierarchical) clustering and classification methods are optimization-based methods, in the sense described by Windham (1987) in this Journal. This paper gives some large sample properties for estimates derived by such methods. Under appropriate conditions, such estimates converge with probability one to a limit, and are asymptotically normally distributed around that limiting value. The conditions are satisfied by most of the common examples of optimization-based methods. Prepared for the 2nd International Conference, International Federation of Classification Societies, Charlottesville, VA, 1989. Supported in part by summer research funds, Graduate School of Business Administration, University of Colorado at Denver.